In fact, these words have been part of our vocabulary for long enough that they sometimes get used interchangeably. But that can lead us to make some false assumptions.

Take big data, for instance. What does big data mean to you? Simply put, big data refers to the vast collection of structured and unstructured data created from various sources within an organization—everything from file stores to databases to social media to sensor data to cloud to email and much more. Gartner defines big data like this.

Structured data is data that is stored in a highly organized manner such as a spreadsheet or a relational database like SQL – with a consistent row and table format, for example. It’s easy to query. We can write a query, run it against structured data, and receive a return, because the data has a set structure, and it’s in a format that is expected and consistent.

Unstructured data is essentially everything else. It includes email, social media content, documents, IoT and sensor data, and more. International Data Corporation (IDC) estimates the world will create 180 zettabytes (that’s 180 trillion gigabytes) of data in the Digital Universe in 2025. Much of that will be unstructured data.

Data analytics is the process of extracting, inspecting, and modeling data to gain insights. With data analytics, the goal is to identify patterns or anomalies or to develop conclusions based on applying an automated or mechanical process against data. For more information about data analytics, take a look at this article.

Big data analytics refers to the process of performing data analytics against a big data repository. It’s about applying data analytics techniques to the vast store of structured and unstructured data with the goal of uncovering insights that may otherwise be unknown or even impossible to decipher.

Do you need a solution for big data before you can use data analytics?

There is a lot of value to be gained by harnessing big data. Big data analytics can help streamline manufacturing, improve processes, cut costs, reveal customer preferences, increase competitiveness, and more.

But big data projects are complex and challenging. They take time to implement. There are competing priorities. And, it can be difficult to identify use cases with a tangible return on investment where, at the start, there are so many possible paths yet so many unknowns.

According to a June 2016 survey conducted by Gartner, the number of companies that invested in big data was up 3% compared to 2015; however, the number of companies planning to invest in the next two years was down 6%. In addition, many of those respondents indicated that their big data projects were stuck at the pilot phase.

The good news is this: you don’t have to solve your big data dilemma before you can implement data analytics. Here are some suggestions for gathering insights now:

Get started – Don’t wait. If you wait until you’ve figured out big data, you may be missing out on some real business benefits from data analytics.

Define clear use cases – Make sure you target an area of your business where there is a strong use case and then prove it out. If your business execs can see tangible results, they will be more likely to fund additional efforts.

Begin small - Choose a subset of your data to begin. Don’t boil the ocean trying to get analytics out of every piece of data out there. Taking too big of a bite at first will slow down—and maybe even prevent—your success.

Get organizational buy-in – In a big data survey conducted by NewVantage Partners (as reported in this Forbes article), more than 85% of respondents indicated that they had started programs to create a data-driven culture. But only 37% reported success. The common problems they noted were: lack of understanding by management, organizational alignment, and resistance. Use your early efforts to get your organization culturally and technologically accustomed to the requirements needed to truly leverage data analytics.